Reads were aligned to the mm10 assembly using STAR (2.7.6a). The following alignment QC report was produced:

SRP201470_QC_RnaSeqReport.html

HTSeq (0.11.3) function htseq-count was used to count reads. Counts for all samples were concatenated into the following text file:

SRP201470_htseq_gene.txt

DESeq2 (1.28.1) was used for differential gene expression analaysis, based on the HTSeq counts matrix and the phenotype file provided. Normalized counts from DESeq2 are saved in the following text file:

SRP201470_counts_normalized_by_DESeq2.txt

Normalized counts are obtained from DESeq2 function estimateSizeFactors(), which divides counts by the geometric mean across samples; this function does not correct for read length. The normalization method is described in detail here: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-10-r106

Differential gene expression analysis was done for all comparisons provided in the comparisons file. The following design was used:

design = ~ + Donor + Status

If desired, the design can be modified to include more independent variables. In addition to the partial results displayed in this report, the full set of DESeq2 results for each comparison was saved down in separate text files, with names of the form:

SRP201470_CASE_vs_CONTROL_DESeq2_results.txt

where CASE and CONTROL are pairs of conditions specified in the comparisons file.

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors

IL6_TGFb_3 vs. IL6_3

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_3 3
IL6_TGFb_3 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 92.54 92.54
PC2 4.053 96.59
PC3 1.573 98.16
PC4 1.117 99.28
PC5 0.7204 100
PC6 9.564e-28 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

IL6_TGFb_12 vs. IL6_12

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_12 3
IL6_TGFb_12 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 97.84 97.84
PC2 1.013 98.85
PC3 0.5006 99.35
PC4 0.3936 99.74
PC5 0.2554 100
PC6 1.97e-28 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

IL6_TGFb_48 vs. IL6_48

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_48 3
IL6_TGFb_48 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 91.9 91.9
PC2 4.904 96.8
PC3 1.242 98.04
PC4 1.119 99.16
PC5 0.8387 100
PC6 5.851e-28 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

IL6_rmSAA1_3 vs. IL6_3

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_3 3
IL6_rmSAA1_3 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 91.09 91.09
PC2 5.421 96.51
PC3 1.817 98.33
PC4 0.8567 99.18
PC5 0.8167 100
PC6 1.099e-27 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

IL6_rmSAA1_12 vs. IL6_12

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_12 3
IL6_rmSAA1_12 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 89.35 89.35
PC2 4.832 94.18
PC3 3.038 97.22
PC4 1.81 99.03
PC5 0.9677 100
PC6 1.702e-27 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

IL6_rmSAA1_48 vs. IL6_48

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_48 3
IL6_rmSAA1_48 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 91.54 91.54
PC2 5.263 96.8
PC3 1.7 98.5
PC4 1.019 99.52
PC5 0.4778 100
PC6 3.354e-28 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

IL6_rmSAA1_3 vs. IL6_TGFb_3

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_TGFb_3 3
IL6_rmSAA1_3 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 89.88 89.88
PC2 6.209 96.09
PC3 2.016 98.1
PC4 1.296 99.4
PC5 0.6007 100
PC6 8.633e-28 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

IL6_rmSAA1_12 vs. IL6_TGFb_12

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_TGFb_12 3
IL6_rmSAA1_12 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 92.69 92.69
PC2 3.162 95.86
PC3 2.154 98.01
PC4 1.277 99.29
PC5 0.7144 100
PC6 7.412e-28 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

IL6_rmSAA1_48 vs. IL6_TGFb_48

Samples in this comparison

DE analysis

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in design formula are characters, converting to factors
Comparison Summary
Status Count
IL6_TGFb_48 3
IL6_rmSAA1_48 3

Top 50 genes by p-value

Description of DESEq2 output

Volcano plots

Volcano plot (probes with a q-value <0.05 are present in red)

MA plot

Distribution of adjusted p-values

Dendrogram based on sample distance of regularized log transformed data

Heatmaps for top 30 significant genes

Genes were ranked by adjusted p-values.

Principal component analysis (PCA) Plot based on regularized log transformed data

Compute PCs and variance explained by the first 10 PCs

Variance explained
PC Proportion of Variance (%) Cumulative Proportion of Variance (%)
PC1 89.66 89.66
PC2 6.41 96.07
PC3 1.956 98.03
PC4 1.208 99.23
PC5 0.765 100
PC6 4.508e-28 100

PCA plots are generated using the first two principle components colored by known factors (e.g. Status, Tissue, or Donor)

Dispersion plot

Plot of the maximum Cook’s distance per gene over the rank of the Wald statistics for the condition

Boxplots for top 20 differentially expressed genes

Genes were ranked by pvalue. Counts have been normalized by sequencing depth, with pseudocount of 0.5 added to allow for log scale plotting, using DESeq2 function plotCounts().

Favorite genes

Boxplots for user-defined favorite genes if they exist, and show DE results

## None of user-defined genes are found in current DE results.

Housekeeping genes

Counts have been normalized by estimated size factors using DESeq2. Obtain the count matrix using function DESeq2::counts.

The table shows p-values of house-keeping genes for each comparison. Generally, house-keeping gene expressions do not change significantly in different conditions.

R version 4.0.3 (2020-10-10)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C

attached base packages: parallel, stats4, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: pander(v.0.6.3), biomaRt(v.2.44.4), tidyr(v.1.1.2), DT(v.0.16), DESeq2(v.1.28.1), SummarizedExperiment(v.1.18.2), DelayedArray(v.0.14.1), matrixStats(v.0.57.0), Biobase(v.2.48.0), GenomicRanges(v.1.40.0), GenomeInfoDb(v.1.24.2), IRanges(v.2.22.2), S4Vectors(v.0.26.1), BiocGenerics(v.0.34.0), viridis(v.0.5.1), viridisLite(v.0.3.0), ggplot2(v.3.3.2), RColorBrewer(v.1.1-2), gplots(v.3.1.0) and rmarkdown(v.2.4)

loaded via a namespace (and not attached): bitops(v.1.0-6), bit64(v.4.0.5), progress(v.1.2.2), httr(v.1.4.2), tools(v.4.0.3), R6(v.2.4.1), KernSmooth(v.2.23-18), DBI(v.1.1.0), colorspace(v.1.4-1), withr(v.2.3.0), tidyselect(v.1.1.0), gridExtra(v.2.3), prettyunits(v.1.1.1), bit(v.4.0.4), curl(v.4.3), compiler(v.4.0.3), xml2(v.1.3.2), labeling(v.0.3), caTools(v.1.18.0), scales(v.1.1.1), genefilter(v.1.70.0), askpass(v.1.1), rappdirs(v.0.3.1), stringr(v.1.4.0), digest(v.0.6.25), XVector(v.0.28.0), pkgconfig(v.2.0.3), htmltools(v.0.5.0), dbplyr(v.1.4.4), htmlwidgets(v.1.5.2), rlang(v.0.4.8), RSQLite(v.2.2.1), farver(v.2.0.3), generics(v.0.0.2), jsonlite(v.1.7.1), crosstalk(v.1.1.0.1), BiocParallel(v.1.22.0), gtools(v.3.8.2), dplyr(v.1.0.2), RCurl(v.1.98-1.2), magrittr(v.1.5), GenomeInfoDbData(v.1.2.3), Matrix(v.1.2-18), Rcpp(v.1.0.5), munsell(v.0.5.0), lifecycle(v.0.2.0), stringi(v.1.5.3), yaml(v.2.2.1), zlibbioc(v.1.34.0), BiocFileCache(v.1.12.1), grid(v.4.0.3), blob(v.1.2.1), crayon(v.1.3.4), lattice(v.0.20-41), splines(v.4.0.3), annotate(v.1.66.0), hms(v.0.5.3), locfit(v.1.5-9.4), knitr(v.1.30), pillar(v.1.4.6), geneplotter(v.1.66.0), XML(v.3.99-0.5), glue(v.1.4.2), evaluate(v.0.14), vctrs(v.0.3.4), gtable(v.0.3.0), openssl(v.1.4.3), purrr(v.0.3.4), assertthat(v.0.2.1), xfun(v.0.18), xtable(v.1.8-4), survival(v.3.2-7), tibble(v.3.0.4), AnnotationDbi(v.1.50.3), memoise(v.1.1.0) and ellipsis(v.0.3.1)